{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入一些包\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score,roc_curve,auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import numpy as np\n",
    "import math\n",
    "import xgboost as xgb\n",
    "import toad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_all = pd.read_csv(\"scorecard.txt\")\n",
    "pd.DataFrame(data_all);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 指定不参与训练列名  \n",
    "ex_lis = ['uid', 'samp_type', 'bad_ind'] \n",
    "# 参与训练列名  \n",
    "ft_lis = list(data_all.columns)  \n",
    "for i in ex_lis:      \n",
    "    ft_lis.remove(i) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>uid</th>\n",
       "      <th>td_score</th>\n",
       "      <th>jxl_score</th>\n",
       "      <th>mj_score</th>\n",
       "      <th>rh_score</th>\n",
       "      <th>zzc_score</th>\n",
       "      <th>zcx_score</th>\n",
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       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>act_info</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.919365</td>\n",
       "      <td>0.783020</td>\n",
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       "      <td>0.042640</td>\n",
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       "      <td>0.13</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>dev</td>\n",
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      ],
      "text/plain": [
       "       bad_ind                     uid  td_score  jxl_score  mj_score  \\\n",
       "0          0.0                A1000002  0.825269   0.398688  0.139396   \n",
       "1          0.0                A1000011  0.315406   0.629745  0.535854   \n",
       "2          0.0               A10000481  0.002386   0.609360  0.366081   \n",
       "3          0.0                A1000069  0.406310   0.405352  0.783015   \n",
       "4          0.0               A10001225  0.919365   0.783020  0.033548   \n",
       "...        ...                     ...       ...        ...       ...   \n",
       "65299      0.0  Ab99_96436391994279078  0.864104   0.809891  0.712161   \n",
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       "65301      0.0  Ab99_96436391999814360  0.234014   0.565962  0.532120   \n",
       "65302      0.0  Ab99_96436392001777095  0.027324   0.732346  0.084060   \n",
       "65303      0.0  Ab99_96436392005147255  0.905578   0.927706  0.994447   \n",
       "\n",
       "       rh_score  zzc_score  zcx_score  person_info  finance_info  credit_info  \\\n",
       "0      0.843725   0.605194   0.406122    -0.128677      0.023810         0.00   \n",
       "1      0.197392   0.614416   0.320731     0.062660      0.023810         0.10   \n",
       "2      0.342243   0.870006   0.288692     0.078853      0.071429         0.05   \n",
       "3      0.563953   0.715454   0.512554    -0.261014      0.023810         0.00   \n",
       "4      0.129552   0.544368   0.137676     0.013863      0.023810         0.50   \n",
       "...         ...        ...        ...          ...           ...          ...   \n",
       "65299  0.375159   0.040651   0.971456     0.078853      0.023810         0.03   \n",
       "65300  0.315137   0.890714   0.896967     0.078853      0.142857         1.00   \n",
       "65301  0.702917   0.974988   0.348905     0.078853      0.095238         0.04   \n",
       "65302  0.042048   0.127223   0.274230     0.078853      0.023810         0.00   \n",
       "65303  0.315842   0.959443   0.042640     0.078853      0.071429         0.13   \n",
       "\n",
       "       act_info samp_type  \n",
       "0      0.423077       dev  \n",
       "1      0.448718       dev  \n",
       "2      0.179487       dev  \n",
       "3      0.423077       dev  \n",
       "4      0.166667       dev  \n",
       "...         ...       ...  \n",
       "65299  0.076923       dev  \n",
       "65300  0.076923       dev  \n",
       "65301  0.076923       dev  \n",
       "65302  0.076923       dev  \n",
       "65303  0.076923       dev  \n",
       "\n",
       "[65304 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 开发样本、验证样本与时间外样本  \n",
    "# 划分样本\n",
    "dev = data_all[(data_all['samp_type'] == 'dev')]\n",
    "val = data_all[(data_all['samp_type'] == 'val') ]  \n",
    "off = data_all[(data_all['samp_type'] == 'off') ]  \n",
    "\n",
    "devx=pd.DataFrame(dev);\n",
    "devx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finance_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>35</td>\n",
       "      <td>0.036763</td>\n",
       "      <td>0.039687</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>1.02381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>100</td>\n",
       "      <td>0.063626</td>\n",
       "      <td>0.143098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>act_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>74</td>\n",
       "      <td>0.236197</td>\n",
       "      <td>0.157132</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1.089744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samp_type</th>\n",
       "      <td>object</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>3</td>\n",
       "      <td>dev:68.16%</td>\n",
       "      <td>off:16.67%</td>\n",
       "      <td>val:15.16%</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>dev:68.16%</td>\n",
       "      <td>off:16.67%</td>\n",
       "      <td>val:15.16%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 type   size missing  unique    mean_or_top1     std_or_top2  \\\n",
       "bad_ind       float64  95806   0.00%       2        0.018767        0.135702   \n",
       "uid            object  95806   0.00%   95806  A1000002:0.00%  A9992755:0.00%   \n",
       "td_score      float64  95806   0.00%   95806        0.499739        0.288349   \n",
       "jxl_score     float64  95806   0.00%   95806        0.499338         0.28885   \n",
       "mj_score      float64  95806   0.00%   95806         0.50164        0.288679   \n",
       "rh_score      float64  95806   0.00%   95806        0.498407        0.287797   \n",
       "zzc_score     float64  95806   0.00%   95806        0.500627        0.289067   \n",
       "zcx_score     float64  95806   0.00%   95806        0.499672        0.289137   \n",
       "person_info   float64  95806   0.00%       7       -0.078229        0.156859   \n",
       "finance_info  float64  95806   0.00%      35        0.036763        0.039687   \n",
       "credit_info   float64  95806   0.00%     100        0.063626        0.143098   \n",
       "act_info      float64  95806   0.00%      74        0.236197        0.157132   \n",
       "samp_type      object  95806   0.00%       3      dev:68.16%      off:16.67%   \n",
       "\n",
       "                 min_or_top3     1%_or_top4   10%_or_top5  50%_or_bottom5  \\\n",
       "bad_ind                  0.0            0.0           0.0             0.0   \n",
       "uid           A9996434:0.00%  A999606:0.00%  A99955:0.00%  A3325701:0.00%   \n",
       "td_score            0.000005       0.009613      0.099706        0.500719   \n",
       "jxl_score           0.000013       0.009947      0.099103        0.499795   \n",
       "mj_score            0.000007       0.010508      0.100882        0.503048   \n",
       "rh_score            0.000005       0.009916      0.099948        0.497466   \n",
       "zzc_score           0.000012       0.010186      0.099011        0.501688   \n",
       "zcx_score            0.00001       0.010325      0.099743         0.49913   \n",
       "person_info        -0.322581      -0.322581     -0.322581       -0.053718   \n",
       "finance_info         0.02381        0.02381       0.02381         0.02381   \n",
       "credit_info              0.0            0.0           0.0             0.0   \n",
       "act_info            0.076923       0.076923      0.076923        0.205128   \n",
       "samp_type         val:15.16%           None          None            None   \n",
       "\n",
       "              75%_or_bottom4  90%_or_bottom3  99%_or_bottom2  \\\n",
       "bad_ind                  0.0             0.0             1.0   \n",
       "uid           A3325308:0.00%  A3325154:0.00%  A3325010:0.00%   \n",
       "td_score            0.747984        0.900024        0.990041   \n",
       "jxl_score           0.748646        0.899703        0.989348   \n",
       "mj_score            0.752032        0.899308        0.990047   \n",
       "rh_score            0.747188        0.899286        0.989473   \n",
       "zzc_score           0.750986        0.899924        0.990043   \n",
       "zcx_score           0.750683        0.901942        0.989712   \n",
       "person_info         0.078853        0.078853        0.078853   \n",
       "finance_info         0.02381        0.071429        0.214286   \n",
       "credit_info             0.06            0.18             0.8   \n",
       "act_info            0.346154        0.487179        0.615385   \n",
       "samp_type               None      dev:68.16%      off:16.67%   \n",
       "\n",
       "                            max_or_bottom1  \n",
       "bad_ind                                1.0  \n",
       "uid           Ab99_96436392001380983:0.00%  \n",
       "td_score                          0.999999  \n",
       "jxl_score                         0.999985  \n",
       "mj_score                          0.999993  \n",
       "rh_score                          0.999986  \n",
       "zzc_score                         0.999998  \n",
       "zcx_score                         0.999987  \n",
       "person_info                       0.078853  \n",
       "finance_info                       1.02381  \n",
       "credit_info                            1.0  \n",
       "act_info                          1.089744  \n",
       "samp_type                       val:15.16%  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  探索性数据分析，\n",
    "data_allfenxin=toad.detector.detect(data_all)\n",
    "data_allfenxin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep: 12 drop empty: 0 drop iv: 1 drop corr: 0\n"
     ]
    }
   ],
   "source": [
    "#数据分析\n",
    "dev_slct1, drop_lst= toad.selection.select(dev, dev['bad_ind'], \n",
    "                                                   empty=0.7, iv=0.03, \n",
    "                                                   corr=0.7, \n",
    "                                                   return_drop=True, \n",
    "                                                   exclude=ex_lis)  \n",
    "print(\"keep:\", dev_slct1.shape[1],  \n",
    "      \"drop empty:\", len(drop_lst['empty']), \n",
    "      \"drop iv:\", len(drop_lst['iv']),  \n",
    "      \"drop corr:\", len(drop_lst['corr']))"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "#根据卡方值分箱\n",
    "\n",
    "# 得到切分节点\n",
    "combiner = toad.transform.Combiner()\n",
    "combiner.fit(dev_slct1, dev_slct1['bad_ind'], method='chi',\n",
    "                min_samples=0.05, exclude=ex_lis)\n",
    "# 导出箱的节点\n",
    "bins = combiner.export()\n",
    "bins"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='act_info', ylabel='prop'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "# 根据节点实施分箱\n",
    "dev_slct2 = combiner.transform(dev_slct1)\n",
    "val2 = combiner.transform(val[dev_slct1.columns])\n",
    "off2 = combiner.transform(off[dev_slct1.columns])\n",
    "# 分箱后通过画图观察\n",
    "from toad.plot import  bin_plot, badrate_plot\n",
    "bin_plot(dev_slct2, x='act_info', target='bad_ind')\n",
    "bin_plot(val2, x='act_info', target='bad_ind')\n",
    "bin_plot(off2, x='act_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='act_info', ylabel='prop'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gp9PJ3r27sdvtPPXUGIYOHcxf//oyDRvW74U9REREpHYJCwurtq7VmUEgJCSE7Oxs4OwL6QYFBbkWz83Ozq6yLpbJZOKuu+7i+++/x2azVTnXmWMvNwUBcZuarBHQokVLpk6dTmRkFAaDgcGD72P//v0cPHiAiooKVq78hieffJq5c98jMLAZr7wy6UqVLyIiIlIjF1pIt2fPnmRmZmK321mzZg033ngjS5cudT0itGrVKnx8fLBYLK6xAJmZmW5da0tBQNwmNLQ5eXlHXK/Pto7Ajh3bWf7xB5R+8xW2Xdk47XacTidms5mgoGC6dOlKs2ZBGI1Gbr+9Hz/+uMkTlyIiIiJyTjVZSHf58uXccccd9OrVi4SEBNq3b8+cOXNITk5m8uTJvPLKKwAMGjSIgwcPkpycTHBwMDfddJPb6lb7UHGbgoJ87r//bmbNeoeIiEhmz55Bfn4eTz/9nGvMzp07+PtTY/hTq1YYgAqTiR0nTnBt6gB+PF7EtH/N463Z/6Bx4ybMm/cOmzf/yCuvvO6xaxIRERHvcDHtQ+sqBQFxq5qsI7B06WI+fm8+0RYLHRo3pmNQEIaCfADKzRZ+PH6crBMnKAhozMin/kJQsPuelRMREREBBYFaTUGgfrMfLcC2Iwvb9m3YdmThKDwGgLFpIJbYeCxxbbDGxGM8y6x9ERERkV9LQaAWUxDwHk6nE3vu4ZPBIAvbz9txlpYAYAptjiW2TWU4iInF6NvQw9WKiIhIfaAgUIspCHgvp8OB/cA+yrdnVYaD7J/BVg4GA+bwiF+CQetoDBarp8sVERGROkhBoBZTEJBTnBU2KvbspvzkY0QVe3aBwwFmM5ZWrStDQWwbzBGRGEymC55PREREREGgFlMQkHNxnjiBLXsHtu1ZlO/Iwn6gsnWXoYEP5uhYrLFxWGLbYGoehsGoDroiIiJSnYJALaYgIDXlKC7C9vP2k8FgG44juQAY/BphiY3HGhePJSYeY7Mgty7jLSIiInWHgkAtpiAgl8p+tMDVjUgdiURERORsFARqMU8EgcZNG2I16xnz+sTpdFJ+8CDFmzdTsmULxVu34iguBsDaogV+7drh164dDdu0weRXN/8yKK+wc6ygxNNliIiI1CkKArWYJ4JAcLA/077eckXfU64wh4OA/ByaHdhNs4O7aHpoHyZ7BU6DgWPNmpPfohV5Ya0oCG2Jw2zxdLU18liPtuTmHvd0GSIiInWKNwQBs6cLEKlVjEYKg5pTGNSc7Ku7YLBX0CT3IM0O7KLZgd1EbVpD9MbV2E0mjoa0JC+sFfktWnEsKAynJh6LiIhIHaIgIHIeTpOZguYRFDSPYEfH7phsJ2h6aB/NDu6m2YHdxK/7BtZ9Q4XFSn7zCPJO3jEoahoMmngsIiIitZiCgMhFsFsacCQihiMRMQBYykoIPLjn5KNEuwnZ+zMAJ3wakh8WeTIYRFHq31jBQERERGoVBQGRX8Hm05DDrRM43DoBAJ+iQpod3E3ggco7BmHZWwEobRRAXlgr8lpEkRcWSXnDRp4sW0RERERBQORyKmsUwP649uyPaw9OJ37H8ml2YDeBB3cTujuL8O2bADjepBn5LaIq5xg0j6CigY+HKxcRERFvoyAg4i4GA8VNmlHcpBl72nWs1pEofNsPtNr8fZ3uSCQiIiJ1l4KAyJVyto5EOQdcE4/VkUhERESuJAUBEQ9xmswUhEVSEBZZtSPRyYnH6kgkIiIi7qQgIFJLXHJHooAmHqxaRERE6ioFAZFaqqYdiUoaNT4ZDNSRSERERGpOQUCkjqh5R6Ig18Tj/OYRHq5aREREaiuD0+l0erqIS1FQUExFheOKvmdwsD/Tvt5yRd9TpEYcDgLyD9PswB6aHdxF00P7MNkrcBoM+LZujaFVDJbYeCytozFYrJ6uVkREpNYzm400bern6TLcSkHgIigISF1xekei9sW5lP78MzgcYDZjadUaS1wbLLHxmMMjMZhMni5XRESk1vGGIKBHg0TqodM7EvXr0ZbDe3OpyP6Z8h3bsG3PouR/nwNgaOCDOToWa1w8ltg2mEKbY1CrUhEREa+gICDiBYw+PljbJmJtmwiAo7gI247t2HZso3xHFsVbfgTA4NcIS2z8L8GgWZAnyxYRERE3UhAQ8UJGv0Y06PAbGnT4DQD2gnxsO7JO/rON8h/WVY5rGlg5tyCuDdaYeIwBAZ4sW0RERC4jBQERwdQ0EFOnrvh06orT6cSec/iXUPDjD5xYu7pyXGgYlrh4LDHxWGLiMPr6erhyERERuVQKAiJShcFgwBzaHHNoc3y79cDpcFCxfx+2Hduw7ciiLPNbyjLSwWDAHB75SzBQRyIREZE6RUFARM7LYDRiiYjEEhEJN92Ks8JGxe5dlO/IwrZ9G6UrvqR0+TJ1JBIREaljFARE5KIYzBYsMXFYYuKgdzKOsrKadSRqHobBYPBw9SIiInKKgoCI/CrVOhIVHcf28w51JBIREanlFARE5LIyNvJXRyIREZE6QEFARNzq7B2JKiceqyORiIiI57g1CKSlpfHuu+/i5+fH1KlTiYiIcO178803+eCDD2jatCkAU6dOJS4uzp3liIiHVe1IdGPVjkTbz9GRKLYNlqjW6kgkIiJymbktCOTl5TFnzhwWLVrE2rVrmTJlCjNnzqwy5rHHHiM1NdVdJYhILaeORCIiIp7jtiCQkZFBYmIiDRs2JCkpiXHjxuFwODAaja4xjRs3dtfbi0gdpI5EIiIiV47bgkBubi6tW7cGwGQy4e/vz9GjRwkMDHSN+fvf/85rr73G9ddfz1NPPYXZXLWcwsJCCgsLq2yzWq2EhIS4q2wRqUXUkUhERMR93DpHwOl0uv5cXFxc5Re7AQMG8Lvf/Q4fHx8efvhhPv/8c/r371/l+Pnz5zNjxowq2zp27MiCBQto2tTPnaWL1CvBwf6eLuHyCPaH1i2gVw8AbHl5FG/eTPHmzZRs2ULRyY5ElqAgGrZrh1/btvi1bYu5SRMPFi0iIlI7uS0IhISEsHHjRgCKioooLCys8ihQ8+bNXX++7bbbyM7OrnaOoUOHVptDYLVWThgsKCimosLhjtLPqd58mRKvk5t73NMluIkVEq7BmnANltM7Em3PonDtWo59/TVwWkei2DZYomPVkUhERC7IbDbW+x+e3RYEkpKSmD59OiUlJWRmZtKjRw+WLFlCTk4OQ4YMYenSpSQnJ1NaWsq3337LwIEDq50jICCAAPUWF5EaUEciERGRi+O2IBAYGMiIESMYOHCgq33osmXL2L9/PyaTid27dzNgwADy8vK4/fbb6dWrl7tKEREvdLaORLbduyoXNlNHIhERuczO1zY/Pz+fMWPGkJuby+23386oUaOw2Wy8/PLLfP/99zgcDl544QWuvvpqMjMzGTVqFC1btgRg2LBhbuuyaXCe/iB/HeKpR4Omfb3lir6nyK/1WI+29fjRoEtX2ZFox8lWpVnYD+4H6l5Hom+/zeDtt2dQXl5OTEwc48b9BT+/RjUac+JEGa+99jJbt27G4XDSrl0ijz8+lgYNfDx0NSIitcfFPBqUl5fHoEGDXG3z09LSqrTNnzRpElFRUQwePJjBgwczceJEfH19Wb9+PSkpKaxYsYK3336bhQsXkpmZySeffMJLL73krktzMV54iIhI/VPZkegqGvX7HU3HPEXgc5Pxv/cBGlxzLfbcwxQv+oijf32J/InPUPjeXMoyV2LPO+LpsqsoKChg8uQJTJr0CgsWfESLFi15660ZNR4zf/4/sNvtzJu3gPnzF3DixAn++c95HrgSEZG67cy2+evXr8fh+OUH6/T0dLp27YrJZKJ3796sWLGCVq1a0b9/fwwGA9deey0HDhxwjb9SLfbd2jVIRKSuMDbyp0GHjjTo0BEAe0G+6zGi8h1ZlG+o7EhkbBroeozIGhOP0YPzmNauXU3btu2IiIgEIDX1LoYNG8zjj4913cU435hrrulI8+ZhrvVd4uPbkJ290zMXIyJSSx08eBC73V5l25nzWC/UNj8/P5/IyMq/h0NDQ1m3bl2V8/3www+0a9fO9XrlypWkpKTQokULJkyY4LbW+QoCIiJnYWoaiKlTV3w6dcV5Rkei8k0bOLFmVeU4D3YkOnz4MCEhoa7XwcEhFBcXU1JS7Ho86HxjOnfu6tp+6NBB0tIW8Oc/P3PF6hcRqQuGDBnC/v37q2x75JFHGD16dJVt52ub73Q6XfvP3Gez2ZgxYwZPPPEEAB06dGDKlCnExMTwyiuv8PrrrzN58uTLfl2gICAickFn70i09+QdA891JHI6zz5Pymg0XdSYrVu38PTTT3DnnQPp1q375S1SRKSOe++99856R+B0F2qbHxQUxO7du4mPjyc7O9v1C7/T6WT8+PF0796dTp06AeDj40NiYuVCmnfeeScvvPCC265NQUBE5CJVdiRqhSWilUc7EoWGNmfz5h9dr48cycXfPwDf0+5KXGjMF18s5bXXXuaxx/7Mb39722WrTUSkvggLC7vgmPO1zX/ggQfo2bMnmZmZxMTEsGbNGtcv/FOnTsVmszFq1CjXuRYvXkzPnj1p0KABy5cv56qrrnLbtSkIiIj8SgazBWtMHNaYOOid/EtHou1Z2HZkUfK/zyvHXeaORJ07d2XGjNfZu3cPERGRfPLJh3TvfmONx3z11Re8/vpUpk2bQUJCu7O9hYiI1MD52uYDjBw5kjFjxrBw4UKSk5NJSEhgxYoVvPPOOyQmJtK/f38Axo8fj8lkYvjw4Rw5coS4uDimTJnitrrVPvQiqH2o1EVqH+p5jqLj2H7eXjm/YMc2HCe7Dxka+WOJjcMaezIYNAu66HOvWpXB7Nkzqaiw0bJlOOPHT+DAgf1MmTKJefPeP+eYgIDG3H13KkVFxwkK+mUSWvv2HXj88bGX58JFROowb1hZWEHgIigISF2kIFD7nNmRyHm8EPilI5E1Nh6LhzsSiYh4O28IAno0SETkCqvekeiQa+Jx+cba0ZFIRETqPwUBEREPquxIFIY5NKxqR6Lt27Dt2E7Zas90JBIRkfpPQUBEpBap0pHo5t/+0pFo+zZsO7KuWEciERGp/xQERERqsSodieCMjkTbfulI5OODJToWS+zl6UgkIiL1n4KAiEgdYvTxwdr2KqxtK/tKn9mRqPzkmgG/dCSqvGNwKR2JRESkflMQEBGpw4yN/GnQoSMNOnQETnYkOvkYUfmOLMo3rKscd3pHoth4jP7qSCQi4u0UBERE6hFT00BMna/Hp/P1v3QkOvkYUZWORM3DKicdx8VXdiTyUUciERFvoyAgIlJPVelIlHQjTru9siPRyTUMylavpCxjBRiNJzsSVT5GZIlqjcFs8XT5IiLiZgoCIiJewmAyYYmMwhIZVdmRyGbDtnvnyTsGWZQu/z9Kv1wKZguW1tGVoSCuDeaWERiMRk+XLyIil5mCgIiIlzJYLFhj22CNbQOAo7QU284d2HZsw7Y9i5Iln8GSzzD4+mKJjnOtYWAKCVVHIhGRekBBQEREADD6+tIgsT0NEtsD4CgspPznrF8mH/+0sXJcQGPX3QJLbDymJk09WbaIiFwiBQERETkrY0AAPr+5Dp/fXIfT6cSRd+RkN6JtlG/bwol1awEwBYe41i+wxMZhbOjn4cpFRKQmFAREROSCDAYDpqBgTEHB+HTthtPhwH7ogGv9grLv11C2KgMMBkwtwivblMa1wdI6GoO1gafLFxGRs1AQEBGRi2YwGjG3CMfcIhzfG2+u7Ei0Z3dlm9IdWZRmrKA0/UswmTC3an1y/YI2mCNbYTCZPF2+iIigICAiIpeBwWSq7DTUOpqGt/bBWX4CW/ZObNsrg0HJsiXwf4sxNGiAuXVM5YrHcfGYmrdQRyIREQ9REBARkcvOYG2AtU1brG3a4gc4ioux/bz9ZDDYRvHWzZXj/BphiY2rDAax8RibBakjkYjIFaIgICIibmf086PB1dfQ4OprALAfLXB1I7Lt2Eb5D+srxzUNdHUkssbEYwwI8GDVIiL1m4KAiIhccaYmTTF16opPp644nU7sOYdd6xeU//gDJ9aurhwXGla5fkFcGyzRsRh9fD1cuYhI/aEgICIiHmUwGDCHNscc2hzfbjfidDio2L/XdcegbPW3lGWkg9GIOTzStX6BpVVrDBaLp8sXEamzFARERKRWMRiNWCJaYYloBTf/FqfNhm13duVjRNu3UfrVMkq/XApmS+UE5ZOPEplbRmjisYjIRVAQEBGRWs1gsWCNjccaGw+39cVRVopt5w5s2yvnF5Qs+QyWfIbB1xdLdFzlo0SxbTCFhGrisYjIeSgIiIhInWL08aVBu/Y0aNceAMfxwpMrHlfeMSj/aWPluIDGJ1c8rrxjYGrS1JNli4jUOgoCIiJSpxn9A2jwm+to8JvrALDnHansRLQ9i/JtWzixbm3luKBg1/oFlph4jH5+nixbRMTjFARERKReMTULwtQsCJ8u3XA6HNgPHXSteHxi/VrKVmeAwYCpRUvX+gWW6BgM1gaeLl1E5IpSEBARkXrLYDRibtESc4uW+Pa4GafdTsXe3ZVtSndsozRjBaXpX4LJhDkyCuupiceRURhMJk+XLyLiVgoCIiLiNQwmE5aoaCxR0TS89Tac5eXYsn8+ueJxFiVf/A+WLQGrFUt0LJbYeKyxbTCFtVBHIhGpdxQERETEaxmsVqxt2mJt0xY/wFFSjG3H9srFzXZkUbJ1MyWAwa8Rltg416NExmZB6kgkInWegoCIiMhJxoZ+NLj6GhpcfQ0A9qMFrvULbDuyKP9hfeW4poGubkTWmHiMAQEerFpE5NIoCIiIiJyDqUlTTNd1wee6LjidTuy5h13rF5T/+AMn1q6uHBca5lq/wBIdi9HX18OVi4hcmIKAiIhIDRgMBswhzTGHNMe3Ww+cDgcV+/dVPka0PYuyzG8py0gHgwFzRKuTdwzisbSKxmCxeLp8EZFqFAREREQugcFoxBIRiSUiEm66FWeFDdvuXa7HiEpXfEHp8v8DswVLVGsscZXzC8zhkZp4LCK1goKAiIjIZWAwW7DGxGGNiQPAUVZKxc6fKT95x6BkyWeV43x8scTEVj5GFBePKaS5Jh6LiEcoCIiIiLiB0ccXa7ursLa7CgDH8UJsP2+n/NTE4582VY4LCMASE++6Y2BqGujJskXEiygIiIiIXAFG/wAaXHMtDa65FgB73pHKScfbsyjP2sqJ9d9VjgsKdi1sZomJx+jn58myRaQec2sQSEtL491338XPz4+pU6cSERFRbczkyZPZsmUL//znP91ZioiISK1iahaEqVkQPl264XQ4sB86WBkMdmRxYv13lK1eCQYDphYtXesXWKJjMFgbeLp0Eakn3BYE8vLymDNnDosWLWLt2rVMmTKFmTNnVhnz888/8/3339OwYUN3lSEiIlLrGYxGzC1aYm7REt8eN+O026nYuxvb9izKd2yjNCOd0vQvwWTCHBnlumNgjozCYDJ5unwRqaPcFgQyMjJITEykYcOGJCUlMW7cOBwOB8aTnRKcTidTpkzhwQcf5F//+tdZz1FYWEhhYWGVbVarlZCQEHeVLSIi4nEGkwlLVDSWqGga3nobzvJybNk/Vy5utmMbJV/8D5YtAasVS3Qslth4rLFtMIW1UEciEakxtwWB3NxcWrduDYDJZMLf35+jR48SGFg5Ceqrr74iLCyMxMTEc55j/vz5zJgxo8q2jh07smDBApo21TOTIjUVHOzv6RJE5Ndq2QySOgNgLyqieOtWSjZvpnjzZko+/4QSwOTvT8OEBPzatcOvXTssISHqSCQi5+TWOQJOp9P15+LiYtdfRuXl5cyePZvZs2dTUlJyzuOHDh1KampqlW1WqxWAgoJiKiocbqj63PRlSuqq3Nzjni5BRC63Vm0wtWpDQJ9U7EcLTt4tyKI4K4vja9cCYGzS1NWNyBrbBmNAgIeLFqk7zGZjvf/h2W1BICQkhI0bNwJQVFREYWEhjRs3BuD777/n6NGjPPTQQ5SXl7Nnzx5efPFFnnnmmSrnCAgIIEB/aYmIiJyXqUlTTNd1wee6LjidTuy5Oa4Vj8t/3MiJtasrx4U2r1y/IDYeS0wcRl9fD1cuUn+cr0lOfn4+Y8aMITc3l9tvv51Ro0Zhs9l4+eWX+f7773E4HLzwwgtcffXV2O12Jk6cyHfffUdsbCwvv/wyPj4+bqnZ4Dz9Z/vLKD8/n4EDB7Jo0SJWrVrFxx9/TJ8+fcjJyeGBBx5wjdu3bx/jxo276K5BnrojMO3rLVf0PUV+rcd6tNUdAREv5nQ4sB/YR/n2yvkFtuyfwWYDgwFzeCSWuPjKcBAVjcFi8XS5IrXGxdwRyMvLY9CgQa4mOWlpaVWa5EyaNImoqCgGDx7M4MGDmThxIr6+vqxfv56UlBRWrFjB22+/zcKFC1m2bBmfffYZ06dP55VXXiE0NJShQ4e65xrdclYgMDCQESNGMHDgQFcyWrZsGfv373fXW4qIiMgZDEYj5vBIzOGRcFMvnBU2KnbvonxHFrbt2yhd8SWly5eB2Vw5Qfnko0Tm8EhNPBapoQs1yUlPT+fuu+/GZDLRu3dvVqxYwUMPPUSrVq0AuPbaazlw4AAAK1asoEuXLgD06dOHadOm1b0gADBgwAAGDBjgej18+PBqY8LDw7WGgIiIyBViMFuwxMRhiYmD3sk4ysqoyN7humNQsuSzynE+vlhiYivvFsTFYwppronH4pUOHjyI3W6vsu3Mx9cv1CQnPz+fyMhIAEJDQ1m3bl2V8/3www+0a9fOda7o6GjX2JycHPdcGFpZWERExKsZfXywtr0Ka9urAHAUHce2I+vkHYMsyn/aVDkuIABLTLzrjoGpaaAnyxa5YoYMGVLtiZZHHnmE0aNHV9l2riY5p/ad2n/mPpvNxowZM3jiiSdc2xwOx1nHXm4KAiIiIuJibORPg2uupcE11wJgzz/iWtisfPs2Tqz/rnJcUHDlwmaxbbDExmH0a+TJskXc5r333jvrHYHTna9JDkBQUBC7d+8mPj6e7Oxs15pYTqeT8ePH0717dzp16uQ6V3Z2Nt26dasy1h0UBEREROScTIFBmLoE4dPlhsqORIcOYtuxjfLtWZxY/z1lq1dWjmsRfnLF43gsrWMxNGjg4cpFLo+wsLALjklKSmL69OmUlJSQmZlJjx49WLJkiatJTs+ePcnMzCQmJoY1a9YwefJkAKZOnYrNZmPUqFGuc/Xs2ZNPP/2Ue++9l8zMTG688Ua3XZuCgIiIiNSIwWDAHNYCc1gLfLvfhNNup2LvnspuRDuyKF35NaVfLwejEXOrKCyxbbDGxmOOjMJg1lcOqb8u1CRn5MiRjBkzhoULF5KcnExCQgIrVqzgnXfeITExkf79+wMwfvx4br75ZlauXElycjKxsbGMGTPGbXW7rX2ou6l9qEjNqH2oiFwpzvJybLt2utYwqNi/F5xOsFqxtI6pDAZx8ZjCWqojkdR6WlBMREREpIYMVivW+ASs8QkAOEpKsP283XXHoOS/n1ACGBr6YYmNc90xMAYF1+qORN9+m8Hbb8+gvLycmJg4xo37C35nzIk415gTJ8p47bWX2bp1Mw6Hk3btEnn88bE0aOCeBaJELobuCFwE3RGQukh3BESktrAfO4rt5PoFth1ZOI4dBcDYpGnlascn7xgYAxqf/0RXUEFBAffdN5C33vo7ERGRzJpV+Rz4E088VaMxc+bM4vDhQzzzzPM4nU4mTvwLERGR/L//95AHr0pqQncERERERC4TU+MmmK7tjM+1nXE6nTiO5FJ+MhSUb97Eie8yK8eFND+54nE8lpg4jL4NPVbz2rWradu2HRERlT3gU1PvYtiwwTz++FjXXYzzjbnmmo40bx7mWlgqPr4N2dk7PXMxImdQEBAREZErzmAwYAoOwTc4BN8buuN0OLAf2E/5yfkFZWtWU7byazAYMIdHngwGbbBEtcZgsV6xOg8fPkxISKjrdXBwCMXFxZSUFLseDzrfmM6du7q2Hzp0kLS0Bfz5z89csfpFzkdBQERERDzOYDRiDo/AHB4BPXvhrLBRsWe3645B6YovKV2+DMxmLFHRlXcL4tpgbhmBwWRyW11O59kfQzYaTRc1ZuvWLTz99BPceedAunXrfnmLFLlECgIiIiJS6xjMFizRsViiY6F3Mo6yMiqyd1C+PQvbjm2U/O9z+N/nGHx8sETHue4YmEKbX9aJx6Ghzdm8+UfX6yNHcvH3D8DX17fGY774YimvvfYyjz32Z37729suW20iv5aCgIiIiNR6Rh8frG2vwtr2KgAcRccr5xbsyKpc+XjzJgAM/gEnVzyuvGNgahr4q963c+euzJjxOnv37iEiIpJPPvmQ7t1vrPGYr776gtdfn8q0aTNISGj3q2oRudzUNegiqGuQ1EXqGiQi3sCen+fqSFS+IwtnUeXfe8ZmQVhj27gmHxvPaPtZE6tWZTB79kwqKmy0bBnO+PETOHBgP1OmTGLevPfPOSYgoDF3351KUdFxgoJCXOdr374Djz8+9vJcuLiNN3QNUhC4CAoCUhcpCIiIt3E6ndgPH8R28jEi2887cJ4oA8AU1hJLXHxlOIiOxdCggYerldrKG4KAHg0SERGResVgMGBu3gJz8xb4du+J026nYt8e1x2DspXfUPb1V2A0Yo6MwhIbjzWuDebIKAxmfTUS76H/t4uIiEi9ZjCZsLRqjaVVa7ilN05bObbsnZXBYMc2Sr9cSukX/wOLFUt0jGthM1NYSwwn+/+L1EcKAiIiIuJVDBYr1vgErPEJADhKS7D9vP3kHYMsSv77CSWAoaEflti4ymAQG48xKPiydiQS8TQFAREREfFqRt+GNLiqAw2u6gCA/dgx190C244syjduoBgwNmla2Y3o5B0DY0BjzxYu8ispCIiIiIicxtS4MaZrO+FzbSecTieOI7mVbUp3bKN88yZOfJdZOS6kuasbkSUmDqNvQw9XLt7qp59+4uDBg/Tq1QuAsrIyfHx8LnicgoCIiIjIORgMBkzBIfgGh+B7fRJOhwP7wf2uhc3K1qymbOXXYDDg2+s2/H57u6dLFi/z2muvkZuby4YNG+jVqxcHDx5kwoQJzJ49+4LHKgiIiIiI1JDBaMTcMgJzywjoeQvOigoq9uzCtiMLc2QrT5cnXuiLL75g8eLFpKamAhAWFsahQ4dqdKyCgIiIiMglMpjNWKJjsUTHeroU8VK+vr7k5eW5JrKvW7cOi8VSo2MVBERERERE6qgJEybw0EMPsXfvXgYPHsyRI0eYNm1ajY5VEBARERERqaPat2/PwoUL2bVrFw6Hg9atW2MymWp0rFbJEBERERGpox544AHMZjOxsbHEx8djsVi47777anRsje4IHD9+nPnz5/PTTz/h4+ND165dufPOOzFrGW4RERERkSvuhx9+YMOGDezcuZN3333Xtf3QoUPk5ubW6Bw1+ib/2GOPERMTwz333IPJZOKjjz5i06ZNTJo06dIqFxERERGRS9agQQPMZjN2u53CwkLX9tatW1cJBudToyBw5MgR3nnnHdfr66+/nn79+l1kuSIiIiIicjkkJCSQkJBAbGwsXbp0uaRz1CgIXHPNNWzdupWEhAQAcnNzXX8WERERERHPiI6OZubMmRw4cACHw+Ha/tJLL13w2BoFgYyMDP7zn//QsGFDnE4n5eXlGAwGOnXqhMFgYM2aNZdevYiIiIiIXJLRo0fTrVs3du3axbBhw/jpp59qPI+3RqO++OKLX1WgiIiIiIhcfuXl5YwePZrS0lLCw8O59dZbue+++3jkkUcueGyN2/78+OOPfPvttzgcDrp160b79u1/VdEiIiIiIvLrhIeHs2vXLpKTk5k2bRo33ngjx48fr9GxNVpHYMGCBTz33HNYrVZ8fHx4/vnnef/9939V0SIiIiIi8us8/fTTBAUFkZiYyMCBA9m5cycTJ06s0bE1uiPw/vvv88EHH+Dj4wPAoEGDGDhwIPfcc8+lVy0iIiIiIr/K3/72N/7yl78A0KtXL3r16lXjY2u8srDdbnf9+fQZySIiIiIi4hm5ubns2rXrko6t0R2B3//+96SmptKjRw8AvvnmG0aOHHlJbygiIiIiIpdHkyZNSE1NpWvXrphMJtf2GTNmXPDYGgWB/v37c/XVV7Nq1SoABg8eTExMzCWWKyIiIiIil0Pfvn3p27fvJR1boyBw9913s3DhQqKjoy/pTURERERE5PLr3LnzJR9boyDQpUsXPv30U1JSUi75jURERERE5PI4tbCvzWbD6XRitVoBKC0tpUWLFixduvSC56hREFixYgV/+9vfmDhxIiaTCafTqRWFRUREREQ8ZO3atQCMGTOGcePGERwcDMD27dv55z//WaNz1CgIfPDBB/z3v//lp59+okGDBlx//fUkJSVdYtkiIiIiInI5ZGVluUIAQFxcHBs2bKjRsTUKAuPGjaO8vJxu3bphMpl4++23Wbt2LY899th5j0tLS+Pdd9/Fz8+PqVOnEhER4dr3z3/+k48//pijR49yxx138Oijj2IwGGpUtIiIiIiIQIcOHXj88ccZOHAgxcXFfPnll7Ro0aJGxxqcTqfzQoP69evHZ5995npdXl5O//79Wbx48TmPycvLY9CgQSxatIi1a9eSlpbGzJkzXfvXrFnDtddeS0VFBb169eLf//53jYsGKCgopqLiyq5nEBzsz7Svt1zR9xT5tR7r0Zbc3JotNS4iIiKVzGYjTZv6ebqMC7LZbHzwwQesXr0am81Ghw4duPfee2nUqNEFj63RHYE2bdqwb98+wsPDgcogEBUVdd5jMjIySExMpGHDhiQlJTFu3DgcDgdGY+UaZp07d6aiooItW7YQEBBASEhITUoREREREZGTLBYL99xzD/fcc89FH1ujILB3714GDRrk+rJ+7NgxHA4HqampAHz88cfVjsnNzaV169YAmEwm/P39OXr0KIGBga4xDz74IKtWrWL+/PmYzdVLKSwspLCwsMo2q9Wq0CAiIiIiAixevJjXXnuNnJwcfH19KSsrIzw8/LxP7pxSoyDw17/+9ZIKO/2po+Li4mpzAP7xj3+wc+dORo8ezfz58wkKCqqyf/78+dVWRevYsSMLFiyoE7dqRGqL4GB/T5cgIiIibvDGG28wf/58pk+fzvPPP8++ffv48MMPa3RsjYJAy5YtL7qokJAQNm7cCEBRURGFhYU0bty42rjo6GhuuOEGMjIy6N+/f5V9Q4cOdd11OOVUj1RPzREQqYs0R0BEROTiXOwcgfM1ycnPz2fMmDHk5uZy++23M2rUKAA+/PBDZs2axc0338wzzzwDwEcffcSrr77qegLmz3/+M926dTvn+zZp0oTw8HDatWvHqlWruOWWW8jMzKzZNdb46i5SUlIS06dPp6SkhMzMTHr06MGSJUvIycmhf//+fPDBBwwfPpyysjJWrlzJb3/722rnCAgIICAgwF0liojUed9+m8Hbb8+gvLycmJg4xo37C35+VSeILV26mPff/ycGgwEfHx/+9KcnSEhox+uvv8qGDetd444cyaFZsyDmz194pS9DRKROy8vLY86cOa4mOVOmTKnSJGfWrFn06tWLwYMHM3jwYG655RYSEhLo1KkTKSkpHD9e9Qe7IUOG8Mgjj9Tovbt3785PP/1E//79GTp0KLNmzaJVq1Y1OtZtQSAwMJARI0YwcOBAVzJatmwZ+/fvJyAggNLSUu666y6OHTvG7373Ozp16uSuUkRE6qWCggImT57AW2/9nYiISGbNms5bb83giSeeco3Zs2cXs2a9wd///h5BQUGsWpXB008/yUcf/Zc//elJ17iDBw8watQfGD9+gicuRUSk1jp48CB2u73KtjN/rL5Qk5z09HTuvvtuTCYTvXv3ZsWKFSQkJBAZGUl4eDhbtlTtSnm2p2jO5cEHH2Tx4sV8/PHHXH/99QQFBTF06NAaHeu2IAAwYMAABgwY4Ho9fPhw158fffRRHn30UXe+vYhIvbZ27Wratm1HREQkAKmpdzFs2GAef3ysa06WxWJl7Ni/uOZgJSS0Iz8/D5vNhsVicZ3r5ZcnMWjQPcTFtbnyFyIiUosNGTKE/fv3V9n2yCOPMHr0aNfrCzXJyc/PJzKy8u/q0NBQ1q1bd973/PTTT0lLSyMhIYHnnnvuvK1Ax40bx4kTJ0hKSsJkMrFo0SKKior405/+dMFrc2sQEBER9zl8+DAhIaGu18HBIRQXF1NSUux6PCgsrAVhYZVrtDidTt58cxpJST2qhIBVq1aSk3OYu+66+8pegIhIHfDee++d9Y7Amc7XJMfpdLr2n62BzuluvvlmOnToQGhoKE899RTz5893zSk4m6ysrCrrfaWkpNC/f38FARGR+szpPHvDBKPRVG1baWkpL774PDk5h3nttTer7EtLe5977x2GyVT9OBERbxcWFnbBMRdqkhMUFMTu3buJj48nOzv7vK3wmzRpQpMmTQC44447WLp06Xnf+6qrrmL//v2u5j7l5eWuuxMXYqzRKBERqXVCQ5uTl3fE9frIkVz8/QPw9fWtMu7QoUM89NBwTCYjb745G3//XzqgFRQUsHnzj9x0U68rVreISH2TlJTE5s2bqzXJmTt3LgA9e/YkMzMTu93OmjVruPHGG896HqfTyaefforNZqO8vJz09HTat29/1rH9+/cnNTWV7777joEDB5Kamkpqaip33HEHOTk5NapbdwREROqozp27MmPG6+zdu4eIiEg++eRDunev+h+XwsJjjB79IH369GX48AernWPTph9ISEisFh5ERKTmztckB2DkyJGMGTOGhQsXkpycTEJCAlD5GM+xY8coKytjzZo1fPDBBxQVFXHPPfeQl5dH586dz7li8OldiS6VwXn6A011iKfWEZj29ZYLDxSpRR7r0VbrCNRjq1ZlMHv2TCoqbLRsGc748RM4cGA/U6ZMYt6895k//+/8/e9vEx0dW+W4N96YRePGTXj//XfZs2c3Tz31Fw9dgYhI7XSx6wjURQoCF0FBQOoiBQEREZGL5w1BQHMERERERES8kIKAiIiIiIgXUhAQEREREfFCCgIiIiIiIl5IQUBERERExAspCIiIiIiIeCEtKCYitUrjpg2xmk2eLkPkopRX2DlWUOLpMkRELoqCgIjUKlazSet1SJ3zWI+2ni5BROSi6dEgEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdyaxBIS0ujb9++DBo0iL1791bZ98UXX3D33XfTu3dvZs2a5c4yRERERETkDGZ3nTgvL485c+awaNEi1q5dy5QpU5g5cyYATqeTnTt3MnfuXAwGA7fddhu33XYb0dHR7ipHRERE5JJ8+20Gb789g/LycmJi4hg37i/4+TWqNs7pdDJ58gRat47hnnvuA8ButzNt2its2LAOgK5duzFq1KMYDIYreg0iZ+O2OwIZGRkkJibSsGFDkpKSWL9+PQ6HAwCDwcCDDz6Ir68vPj4+tGvXjkOHDrmrFBEREZFLUlBQwOTJE5g06RUWLPiIFi1a8tZbM6qN27Urm0cfHcny5cuqbF+6dDF79uxm/vyFzJu3gA0b1vHVV19eqfJFzsttQSA3N5fWrVsDYDKZ8Pf35+jRo9XG2Ww2tm3bRnx8fLV9hYWF7Nu3r8o/OTk57ipZREREpIq1a1fTtm07IiIiAUhNvYtly5bgdDqrjPvoozRuv70fN998a5XtDoed0tJSbDYb5eXl2Gw2rFbrFatfrpzzPRKfn5/PsGHDSE5Odj0hA/Dhhx9yyy238OKLL7q2lZWV8cc//pHk5GQmTJiA3W53W81uezQIqPIhKS4uPuttsIULF9K1a1eCgoKq7Zs/fz4zZlRN3R07dmTBggU0bep3+QsWqaeCg/09XYJIvafPWf1UXHyUyMhw1/++TZvGUFxcTMOGRho1+uXxoJdemgTATz9toFGjBq7x999/DxkZK/jd726noqKCpKQkUlOTr/yFiFud75F4gFmzZtGrVy8GDx7M4MGDueWWW0hISKBTp06kpKRw/Phx19iFCxfSsmVLpk+fzqOPPspXX31Fr1693FK324JASEgIGzduBKCoqIjCwkIaN25cZcw333zDxx9/zLvvvnvWcwwdOpTU1NQq206l6IKCYioqHG6o/Nz0l7zUVbm5xy88qJbQ50zqqrr0OZOaO368lLIym+t/34qKCgDy80soLXVWG19WZqOo6IRr/DvvzMbPz59PP13KiRMnGDfucaZPf4vBg++9chchl8RsNtb4h+czH4kfN24cDocDo7Hy4Zv09HTuvvtuTCYTvXv3ZsWKFSQkJBAZGUl4eDhbtmxxnWvFihU88MADAPTp04f09HS3BQG3PRqUlJTE5s2bKSkpITMzkx49erBkyRLmzp0LwMaNG5kwYQJvvvlmlUR9uoCAAMLDw6v8ExIS4q6SRURERKoIDW1OXt4R1+sjR3Lx9w/A19e3Rsenpy8nOTkFi8VCo0aN6NOnL+vXf+eucsUNDh48WO1R9cLCwipjLvRIfH5+PpGRlY+XhYaGnvdR99PPdaGxv5bb7ggEBgYyYsQIBg4ciJ+fH1OnTmXZsmXs378fgAcffBAfHx9Gjx6N3W7nhhtuYOzYse4qR0REROSide7clRkzXmfv3j1ERETyyScf0r37jTU+Pj4+geXLl9Gx43VUVFSQkfE17dpd5caK5XIbMmSI6/vrKY888gijR4+usu18j8Q7nU7X/nM9Ln+6Uw12ajL213DrHIEBAwYwYMAA1+vhw4e7/rx69Wp3vrWIiIjIr9a0aSBPP/0s48ePpaLCRsuW4YwfP4GtWzczZcok5s17/7zH//GPY5g27VXuuedOjEYT113XiXvvHXZlipfL4r333qs2YTcgIKDK6ws9Eh8UFMTu3buJj48nOzv7vE+4hISEkJ2dTVRU1AXH/lpuDQIiIiIidd311ydx/fVJVbYFBDQ+awh45pnnq7xu3LgJzz//YrVxUneEhYVdcExSUhLTp0+v9kh8Tk4ODzzwAD179iQzM5OYmBjWrFnD5MmTz3muU2NvuukmMjMzq82XvZzcurKwiIiIiEh9d/oj8XPmzGHs2LEcPnyYffv2ATBy5EiWL1/OHXfcQa9evUhISAAgJSWF6dOn89lnn5GSkkJ5eTmDBg3i4MGDJCcnExwczE033eS2ug3OMxvh1hGe6ho07estFx4oUos81qNtnepmos+Z1EV17XMmIhd2MV2D6irdERARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IbUPFRER8SKNmzbEajZ5ugyRi1JeYedYQYmny6h3FARERES8iNVsUmcuqXMe69HW0yXUS3o0SERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETEC7k1CKSlpdG3b18GDRrE3r17q+zLzs5m2LBhdO7c2Z0liIiIiIjIWbgtCOTl5TFnzhzS0tJ4+OGHmTJlSpX9zZo14/HHH8fhcLirBBERERGRK+J8P4Dn5+czbNgwkpOTmTlzpmv7V199Rb9+/ejfvz+bNm0CIDMzk+uuu46UlBRSUlL4+OOP3Vaz2V0nzsjIIDExkYYNG5KUlMS4ceNwOBwYjZXZIyAggPbt25/3HIWFhRQWFlbZZrVaCQkJcVfZIiIiIiIX5dQP4IsWLWLt2rVMmTKlyhf+WbNm0atXLwYPHszgwYO55ZZbiI6O5oUXXuDf//43BQUFjB071vWl/9Zbb+Wll15ye91uCwK5ubm0bt0aAJPJhL+/P0ePHiUwMLDG55g/fz4zZsyosq1jx44sWLCApk39Lmu9IvVZcLC/p0sQqff0ORNxr9r8GbvQD+Dp6encfffdmEwmevfuzYoVKygqKiIwMJDg4GCCg4Ox2WwcPnwYgMaNG1+Rut0WBACcTqfrz8XFxRgMhos6fujQoaSmplbZZrVaASgoKKai4so+VlSb/w8ocj65ucc9XUKN6XMmdVVd+ZzpMyZ11ZX+jJnNRpo29ePgwYPY7fYq+wICAggICDittvP/AJ6fn09kZCQAoaGhrFu3rsoxp7bn5OQAsHLlSlJSUmjRogUTJkxw29MwbgsCISEhbNy4EYCioiIKCwsvOt2c+S9ZRERERORKGjJkCPv376+y7ZFHHmH06NFVtp3vB3Cn0+naf/q+0+fKntreoUMHpkyZQkxMDK+88gqvv/46kydPvuzXBW4MAklJSUyfPp2SkhIyMzPp0aMHS5YsIScnhwceeMBdbysiIiIictm89957Z70jcLoL/QAeFBTE7t27iY+PJzs7m5CQEEJCQsjOzgYqg8Kp7T4+PiQmJgJw55138sILL7jt2tzWNSgwMJARI0YwcOBA5syZw9ixYzl8+DD79u0D4I033iAlJYXi4mJSUlJYsmSJu0oREREREbkkYWFhhIeHV/nnzCCQlJTE5s2bq/0APnfuXAB69uxJZmYmdrudNWvWcOONN9K+fXuOHj1Kbm4u27Zto0WLFoSEhLB48WJKSkqw2+0sX76cq666ym3X5tY5AgMGDGDAgAGu18OHD3f9+dFHH+XRRx9159uLiIiIiLjd6T+A+/n5MXXqVJYtW+Z6pGjkyJGMGTOGhQsXkpycTEJCAgDPPfccw4cPx2QyMWnSJKByjsHw4cM5cuQIcXFx1VrwX04G5+kPNNUhnposPO3rLVf0PUV+rcd6tK0zkxhBnzOpm+rS50yfMamLPPEZOzVZuD5z68rCIiIiIiJSOykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXkhBQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBdSEBARERER8UIKAiIiIiIiXsitQSAtLY2+ffsyaNAg9u7dW2Vffn4+w4YNIzk5mZkzZ7qzDBERERERt7qU771fffUV/fr1o3///mzatAkAu93Oc889R3JyMo8++ihlZWVuq9ltQSAvL485c+aQlpbGww8/zJQpU6rsnzVrFr169WLRokWkp6ezdetWd5UiIiIiIuI2l/K9t7y8nBdeeIF//OMfvPLKKzz77LMALF++nIKCAv773//SsmVL/v3vf7utbrcFgYyMDBITE2nYsCFJSUmsX78eh8Ph2p+enk7Xrl0xmUz07t2bFStWuKsUERERERG3uZTvvRs3biQwMJDg4GDi4+Ox2WwcPnyYFStW0KVLFwD69OlDenq62+o2u+vEubm5tG7dGgCTyYS/vz9Hjx4lMDAQqLxFEhkZCUBoaCjr1q2rdo7CwkIKCwurbLNarYSEhGAyeWZ6Qwt/X4+8r8ivYTbXrelA+pxJXVSXPmf6jElddKU/Y6e+ax48eBC73V5lX0BAAAEBAa7Xl/K99/RjTm3PyckhNzeX6OjoKtvcxW1BAMDpdLr+XFxcjMFgqLLv1P4z950yf/58ZsyYUWXb3XffzYQJEwgI8MxfYoN+E+WR9xX5NZo29fN0CRdFnzOpi+rS50yfMamLPPUZGzNmTLUfrB955BFGjx5dZdulfO89/a7B2baf6zvy5eK2IBASEsLGjRsBKCoqorCwkMaNG7v2BwUFsXv3buLj48nOziYkJKTaOYYOHUpqamq17SUlJTRs2NBdpYuIiIiIUFRUxKuvvlpt++l3A+DSvveGhISQnZ0NVAaFM7d369btnN+RLxe33WNJSkpi8+bNlJSUkJmZSY8ePViyZAlz584FoGfPnmRmZmK321mzZg033nhjtXMEBAQQHh5e7R+FABERERFxt0aNGp31u+iZQeBSvve2b9+eo0ePkpuby7Zt22jRogUhISGusQCZmZln/Y58ubjtjkBgYCAjRoxg4MCB+Pn5MXXqVJYtW8b+/fsBGDlyJGPGjGHhwoUkJyeTkJDgrlJERERERNzmUr/3PvfccwwfPhyTycSkSZMAuPnmm1m5ciXJycnExsYyZswYt9VtcJ7+QJOIiIiIiHiFutPiQERERERELhsFARERERERL6QgICIiIiLihRQERERERES8kIKAiIiIiIgXUhCQWu3YsWNs3brV02WI1Fv79+9n1apVni5DpN7asGEDX375pafLEDkrBQGptY4dO8aYMWN444036NOnDzt37vR0SSL1Tl5eHs888wzPPvssRUVFni5HpF7ZunUrGzdu5NNPPyU1NVX/HZNaR0FAaqWjR48ye/ZsAgMDeeutt/QXqIibNG3alJtvvpk2bdrQqFEjT5cjUq8kJCQwZMgQpk+fzrXXXqv/jkmtoyAgtVJ6ejo2m43hw4cDkJOTw/fff+/hqkTqn02bNmGxWOjQoQMAWmNS5PIymUyUlZVRUVHB9u3bPV2OSBUKAlLrlJSUsHXrVqKiomjbti0AX3/9Nf379/dsYSL1TEFBAVu2bCEsLIw2bdoAYDAYPFyVSN13eqB2OBx88sknHDp0iH79+nmwKpHqzJ4uQORM5eXl7Nixg549ewLwj3/8g+joaNcXFRG5PNasWcOJEydITEzEYrHgdDoVBEQug1Ofo48//piMjAycTid/+tOfCA8P93BlIlUpCEitU1BQQG5uLl26dOHEiRPMnj2b1157zdNlidQbe/fuxWw2s3//flq1aqW7ASKXUXl5OQsWLGDZsmW0adOG+++/n+joaPz9/T1dmkg1CgJS6zRt2pSoqCiefPJJmjVrRrdu3ejevbunyxKpNzIyMnj99ddp0qQJI0eOpFGjRjgcDoxGPS0q8ms5nU4OHTrEzp07iYuLIyoqSiFAai2DUzPDpBY6evQo77//PjfccAOxsbHqZiJymR09epT58+ezfPlyZsyYQUREhKdLEqlXjh8/zvz58/n000954YUX6Nq1q6dLEqlGQUBExIvZ7XZMJpOnyxCptyoqKrDZbPj6+nq6FJFqFARERERERLyQHggVEREREfFCCgIiIiIiIl5IQUBERERExAspCIiIiIiIeCEFARERqWLjxo3069ePqVOneroUERFxIwUBEZF6wOl0snDhwguOe/LJJ8nJyTnvmLS0NEaMGMFjjz12ucoTEZFaSEFARKQeKC0tZcGCBRcc9+qrrxISEnLeMbt37yY6OlrrC4iI1HMKAiIitZjNZuOFF17grrvuonfv3qxZs4aysjLGjx9P3759GTx4MIcPH2bo0KFkZ2eTkpLC999/f87zpaSksG/fPvbt28fQoUN59tln+e1vf8uYMWNwOp2kpaXx448/8thjj/H111+Tn5/Pww8/TL9+/fj973/PoUOHruDVi4iIO2lBMRGRWm7Tpk20b9+e1atXM3PmTDp16sTx48d5+umnOXz4MCEhIRw4cIBRo0bx6aefnvdcKSkpzJw5E4DU1FQWLFhAZGQkvXv35u9//zvR0dHcd999PP3007Rt25YnnniCq6++mvvvv5/FixezaNEiZs+efSUuW0RE3Ex3BEREarng4GDeeust3n//fQ4cOEB6ejr33XcfBoOB5s2bYzRe2l/lLVq0IDY2FqvVSlxcHEeOHKk2ZuXKlfTt2xeAPn36sHHjRux2+6+6HhERqR0UBEREarF9+/YxYsQIrr76ap599lmcTicOhwODwXBZ38dsNlPTG8S6kSwiUj8oCIiI1GI//vgjkZGRdOvWjW3btgHQtWtX3n//fZxOJ0eOHKGoqIhmzZpRUFBw2X+tv+GGG/jvf/8LwJIlS0hMTMRsNl/W9xAREc9QEBARqcW6du3K8ePH+d3vfscPP/yAn58fo0aN4tChQ/Tt25dHH32UnJwcfH196datG7fffjvp6emX7f2ffvppVq5cSb9+/fjPf/7DxIkTL9u5RUTEszRZWERERETEC+n+rohIPbNp0ybGjx9fbfu7775L48aNPVCRiIjURrojICIiIiLihTRHQERERETECykIiIiIiIh4IQUBEREREREvpCAgIiIiIuKFFARERERERLyQgoCIiIiIiBf6/ypKPj2CWLpNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 因为前三箱的变化趋势与整体不符，4~6合并，最后三箱合并\n",
    "\n",
    "bins['act_info']\n",
    "\n",
    "adj_bin = {'act_info': [0.16666666666666666,0.35897435897435903,]}\n",
    "combiner.set_rules(adj_bin)\n",
    "\n",
    "dev_slct3 = combiner.transform(dev_slct1)\n",
    "val3 = combiner.transform(val[dev_slct1.columns])\n",
    "off3 = combiner.transform(off[dev_slct1.columns])\n",
    "\n",
    "# 画出Bivar图\n",
    "bin_plot(dev_slct3, x='act_info', target='bad_ind')\n",
    "bin_plot(val3, x='act_info', target='bad_ind')\n",
    "bin_plot(off3, x='act_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='samp_type', ylabel='badrate'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制负样本占比关系图\n",
    "data = pd.concat([dev_slct3,val3,off3], join='inner')   \n",
    "badrate_plot(data, x='samp_type', target='bad_ind', by='act_info') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>uid</th>\n",
       "      <th>td_score</th>\n",
       "      <th>jxl_score</th>\n",
       "      <th>mj_score</th>\n",
       "      <th>zzc_score</th>\n",
       "      <th>zcx_score</th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>act_info</th>\n",
       "      <th>samp_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000002</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>-0.029581</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.306411</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000011</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.338661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>0.556362</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A10000481</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>0.556362</td>\n",
       "      <td>-0.323991</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000069</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.029581</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.676743</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>-0.582769</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>A10001225</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>-0.018649</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>dev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95801</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998107976</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95802</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998176292</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95803</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998322771</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>0.033478</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.822677</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95804</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436391998973383</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.018550</td>\n",
       "      <td>0.112487</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>-0.607329</td>\n",
       "      <td>-0.240169</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95805</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Ab99_96436392001380983</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.020754</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>-0.023544</td>\n",
       "      <td>-0.052176</td>\n",
       "      <td>0.625661</td>\n",
       "      <td>1.283319</td>\n",
       "      <td>1.047570</td>\n",
       "      <td>0.438342</td>\n",
       "      <td>off</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95806 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bad_ind                     uid  td_score  jxl_score  mj_score  \\\n",
       "0          0.0                A1000002 -0.000706  -0.029581  0.033478   \n",
       "1          0.0                A1000011  0.000176   0.020754 -0.019345   \n",
       "2          0.0               A10000481  0.000176   0.020754 -0.019345   \n",
       "3          0.0                A1000069  0.000176  -0.029581 -0.019345   \n",
       "4          0.0               A10001225 -0.000706   0.020754  0.033478   \n",
       "...        ...                     ...       ...        ...       ...   \n",
       "95801      0.0  Ab99_96436391998107976 -0.000706   0.020754 -0.019345   \n",
       "95802      0.0  Ab99_96436391998176292  0.000176   0.020754  0.033478   \n",
       "95803      0.0  Ab99_96436391998322771  0.000176   0.020754  0.033478   \n",
       "95804      0.0  Ab99_96436391998973383  0.000176   0.020754 -0.019345   \n",
       "95805      0.0  Ab99_96436392001380983  0.000176   0.020754 -0.019345   \n",
       "\n",
       "       zzc_score  zcx_score  person_info  finance_info  credit_info  act_info  \\\n",
       "0       0.018550  -0.052176    -0.306411     -0.607329    -0.822677 -0.582769   \n",
       "1       0.018550  -0.052176     0.338661     -0.607329     0.556362 -0.582769   \n",
       "2       0.018550  -0.052176     0.625661      1.283319     0.556362 -0.323991   \n",
       "3       0.018550  -0.052176    -0.676743     -0.607329    -0.822677 -0.582769   \n",
       "4       0.018550  -0.052176    -0.018649     -0.607329     1.047570  0.438342   \n",
       "...          ...        ...          ...           ...          ...       ...   \n",
       "95801   0.018550  -0.052176     0.625661      1.283319     1.047570  0.438342   \n",
       "95802   0.018550  -0.052176     0.625661     -0.607329    -0.822677  0.438342   \n",
       "95803   0.018550  -0.052176     0.625661     -0.607329    -0.822677  0.438342   \n",
       "95804   0.018550   0.112487     0.625661     -0.607329    -0.240169  0.438342   \n",
       "95805  -0.023544  -0.052176     0.625661      1.283319     1.047570  0.438342   \n",
       "\n",
       "      samp_type  \n",
       "0           dev  \n",
       "1           dev  \n",
       "2           dev  \n",
       "3           dev  \n",
       "4           dev  \n",
       "...         ...  \n",
       "95801       off  \n",
       "95802       off  \n",
       "95803       off  \n",
       "95804       off  \n",
       "95805       off  \n",
       "\n",
       "[95806 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照上述分箱阈值，对样本做WOE编码\n",
    "t = toad.transform.WOETransformer()  \n",
    "dev_slct3_woe = t.fit_transform(dev_slct3, dev_slct3['bad_ind'], \n",
    "                                      exclude=ex_lis) \n",
    "val_woe = t.transform(val3[dev_slct3.columns])  \n",
    "off_woe = t.transform(off3[dev_slct3.columns])  \n",
    "data = pd.concat([dev_slct3_woe, val_woe, off_woe])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MIRABE~1\\AppData\\Local\\Temp/ipykernel_15092/62095216.py:1: FutureWarning: In a future version of pandas all arguments of Series.sort_values will be keyword-only\n",
      "  psi_df = toad.metrics.PSI(dev_slct3_woe, val_woe).sort_values(0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(95806, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi_df = toad.metrics.PSI(dev_slct3_woe, val_woe).sort_values(0)  \n",
    "psi_df = psi_df.reset_index()  \n",
    "psi_df = psi_df.rename(columns = {'index': 'feature', 0: 'psi'})  \n",
    "psi_013 = list(psi_df[psi_df.psi<0.13].feature)  \n",
    "for i in ex_lis:  \n",
    "    if i in psi_013:  \n",
    "        pass  \n",
    "    else:\n",
    "        psi_013.append(i)   \n",
    "data = data[psi_013]    \n",
    "dev_woe_psi = dev_slct3_woe[psi_013]  \n",
    "val_woe_psi = val_woe[psi_013]  \n",
    "off_woe_psi = off_woe[psi_013]  \n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep: 7 drop empty: 0 drop iv: 4 drop corr: 0\n"
     ]
    }
   ],
   "source": [
    "#再次进行特征筛选\n",
    "dev_woe_psi2, drop_lst = toad.selection.select(dev_woe_psi,\n",
    "                                               dev_woe_psi['bad_ind'],\n",
    "                                               empty=0.6,   \n",
    "                                               iv=0.001, \n",
    "                                               corr=0.5, \n",
    "                                               return_drop=True, \n",
    "                                               exclude=ex_lis)  \n",
    "print(\"keep:\", dev_woe_psi2.shape[1],  \n",
    "      \"drop empty:\", len(drop_lst['empty']),  \n",
    "      \"drop iv:\", len(drop_lst['iv']),  \n",
    "      \"drop corr:\", len(drop_lst['corr'])) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95806, 6)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#线性回归模型\n",
    "dev_woe_psi_stp = toad.selection.stepwise(dev_woe_psi2,  \n",
    "                                                  dev_woe_psi2['bad_ind'],  \n",
    "                                                  exclude=ex_lis,  \n",
    "                                                  direction='both',   \n",
    "                                                  criterion='aic',  \n",
    "                                                  estimator='ols',\n",
    "                                              intercept=False)  \n",
    "val_woe_psi_stp = val_woe_psi[dev_woe_psi_stp.columns]  \n",
    "off_woe_psi_stp = off_woe_psi[dev_woe_psi_stp.columns]  \n",
    "data = pd.concat([dev_woe_psi_stp, val_woe_psi_stp, off_woe_psi_stp]) \n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义回归函数\n",
    "def lr_model(x, y, valx, valy, offx, offy, C):  \n",
    "    model = LogisticRegression(C=C, class_weight='balanced')      \n",
    "    model.fit(x,y)  \n",
    "      \n",
    "    y_pred = model.predict_proba(x)[:,1]  \n",
    "    fpr_dev,tpr_dev,_ = roc_curve(y, y_pred)  \n",
    "    train_ks = abs(fpr_dev - tpr_dev).max()  \n",
    "    print('train_ks : ', train_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(valx)[:,1]  \n",
    "    fpr_val,tpr_val,_ = roc_curve(valy, y_pred)  \n",
    "    val_ks = abs(fpr_val - tpr_val).max()  \n",
    "    print('val_ks : ', val_ks)  \n",
    "    \n",
    "    y_pred = model.predict_proba(offx)[:,1]  \n",
    "    fpr_off,tpr_off,_ = roc_curve(offy, y_pred)  \n",
    "    off_ks = abs(fpr_off - tpr_off).max()  \n",
    "    print('off_ks : ', off_ks)  \n",
    "      \n",
    "    from matplotlib import pyplot as plt  \n",
    "    plt.plot(fpr_dev, tpr_dev, label='dev')  \n",
    "    plt.plot(fpr_val, tpr_val, label='val')  \n",
    "    plt.plot(fpr_off, tpr_off, label='off')  \n",
    "    plt.plot([0,1], [0,1], 'k--')  \n",
    "    plt.xlabel('False positive rate')  \n",
    "    plt.ylabel('True positive rate')  \n",
    "    plt.title('ROC Curve')  \n",
    "    plt.legend(loc='best')  \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义XGBoost函数\n",
    "def xgb_model(x, y, valx, valy, offx, offy):  \n",
    "    model = xgb.XGBClassifier(learning_rate=0.05,  \n",
    "                              n_estimators=400,  \n",
    "                              max_depth=2,  \n",
    "                              class_weight='balanced',  \n",
    "                              min_child_weight=1,  \n",
    "                              subsample=1,   \n",
    "                              nthread=-1,  \n",
    "                              scale_pos_weight=1,  \n",
    "                              random_state=1,  \n",
    "                              n_jobs=-1,  \n",
    "                              reg_lambda=300)  \n",
    "    model.fit(x, y)  \n",
    "      \n",
    "    y_pred = model.predict_proba(x)[:,1]  \n",
    "    fpr_dev,tpr_dev,_ = roc_curve(y, y_pred)  \n",
    "    train_ks = abs(fpr_dev - tpr_dev).max()  \n",
    "    print('train_ks : ', train_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(valx)[:,1]  \n",
    "    fpr_val,tpr_val,_ = roc_curve(valy, y_pred)  \n",
    "    val_ks = abs(fpr_val - tpr_val).max()  \n",
    "    print('val_ks : ', val_ks)  \n",
    "\n",
    "    y_pred = model.predict_proba(offx)[:,1]  \n",
    "    fpr_off,tpr_off,_ = roc_curve(offy, y_pred)  \n",
    "    off_ks = abs(fpr_off - tpr_off).max()  \n",
    "    print('off_ks : ', off_ks)  \n",
    "\n",
    "    from matplotlib import pyplot as plt  \n",
    "    plt.plot(fpr_dev, tpr_dev, label='dev')\n",
    "    plt.plot(fpr_val, tpr_val, label='val') \n",
    "    plt.plot(fpr_off, tpr_off, label='off')  \n",
    "    plt.plot([0,1], [0,1], 'k--')  \n",
    "    plt.xlabel('False positive rate')  \n",
    "    plt.ylabel('True positive rate')  \n",
    "    plt.title('ROC Curve')  \n",
    "    plt.legend(loc='best')  \n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数调用模型\n",
    "def bi_train(data, dep='bad_ind', exclude=None):  \n",
    "    from sklearn.preprocessing import StandardScaler  \n",
    "    std_scaler = StandardScaler()  \n",
    "    # 变量名  \n",
    "    lis = list(data.columns)  \n",
    "    for i in exclude:  \n",
    "        lis.remove(i)  \n",
    "    data[lis] = std_scaler.fit_transform(data[lis])  \n",
    "    devv = data[(data['samp_type']=='dev')] \n",
    "    vall = data[(data['samp_type']=='val')] \n",
    "    offf = data[(data['samp_type']=='off')]\n",
    "    x, y = devv[lis], devv[dep]\n",
    "    valx, valy = vall[lis], vall[dep]\n",
    "    offx, offy = offf[lis], offf[dep]\n",
    "    # 逻辑回归正向\n",
    "    print(\"逻辑回归正向：\")\n",
    "    lr_model(x, y, valx, valy, offx, offy, 0.1)  \n",
    "    # 逻辑回归反向 \n",
    "    print(\"逻辑回归反向：\")\n",
    "    lr_model(offx, offy, valx, valy, x, y, 0.1) \n",
    "    # XGBoost正向\n",
    "    print(\"XGBoost正向：\")\n",
    "    xgb_model(x, y, valx, valy, offx, offy) \n",
    "    # XGBoost反向\n",
    "    print(\"XGBoost反向：\")\n",
    "    xgb_model(offx, offy, valx, valy, x, y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归正向：\n",
      "train_ks :  0.4175617517173731\n",
      "val_ks :  0.3588328912466844\n",
      "off_ks :  0.36930421478753034\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "逻辑回归反向：\n",
      "train_ks :  0.38527996483390414\n",
      "val_ks :  0.37396631393463364\n",
      "off_ks :  0.40858234950836764\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost正向：\n",
      "[20:03:28] WARNING: D:\\Build\\xgboost\\xgboost-1.4.2.git\\src\\learner.cc:573: \n",
      "Parameters: { \"class_weight\" } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[20:03:28] WARNING: D:\\Build\\xgboost\\xgboost-1.4.2.git\\src\\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\machine-learning\\venv\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.4237042266146137\n",
      "val_ks :  0.3595635995538518\n",
      "off_ks :  0.37518296190183736\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost反向：\n",
      "[20:03:29] WARNING: D:\\Build\\xgboost\\xgboost-1.4.2.git\\src\\learner.cc:573: \n",
      "Parameters: { \"class_weight\" } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[20:03:29] WARNING: D:\\Build\\xgboost\\xgboost-1.4.2.git\\src\\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\code\\machine-learning\\venv\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.3938834608675862\n",
      "val_ks :  0.37747626322745126\n",
      "off_ks :  0.39338531134694127\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#训练模型\n",
    "bi_train(data, dep='bad_ind', exclude=ex_lis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#使用单个逻辑回归模型进行拟合\n",
    "dep = 'bad_ind'  \n",
    "lis = list(data.columns)  \n",
    "for i in ex_lis:  \n",
    "    lis.remove(i)\n",
    "devv = data[data['samp_type']=='dev']\n",
    "vall = data[data['samp_type']=='val']\n",
    "offf = data[data['samp_type']=='off' ]\n",
    "x, y = devv[lis], devv[dep]  \n",
    "valx, valy = vall[lis], vall[dep]\n",
    "offx, offy = offf[lis], offf[dep]\n",
    "lr = LogisticRegression()  \n",
    "lr.fit(x, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集\n",
      "F1: 0.029351208260445533\n",
      "KS: 0.4175617517173731\n",
      "AUC: 0.7721143736676812\n",
      "跨时间\n",
      "F1: 0.03797468354430379\n",
      "KS: 0.3588328912466844\n",
      "AUC: 0.7222256797668032\n",
      "跨时间\n",
      "F1: 0.022392178506662464\n",
      "KS: 0.3696948842371405\n",
      "AUC: 0.7436315034285385\n"
     ]
    }
   ],
   "source": [
    "#计算F1,KS值，AUC\n",
    "from toad.metrics import KS, F1, AUC \n",
    "\n",
    "prob_dev = lr.predict_proba(x)[:,1]  \n",
    "print('训练集')  \n",
    "print('F1:', F1(prob_dev,y))  \n",
    "print('KS:', KS(prob_dev,y))  \n",
    "print('AUC:', AUC(prob_dev,y)) \n",
    "\n",
    "prob_val = lr.predict_proba(valx)[:,1]  \n",
    "print('跨时间')  \n",
    "print('F1:', F1(prob_val,valy))  \n",
    "print('KS:', KS(prob_val,valy))  \n",
    "print('AUC:', AUC(prob_val,valy)) \n",
    "\n",
    "prob_off = lr.predict_proba(offx)[:,1]  \n",
    "print('跨时间')  \n",
    "print('F1:', F1(prob_off,offy))  \n",
    "print('KS:', KS(prob_off,offy))  \n",
    "print('AUC:', AUC(prob_off,offy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型PSI: 0.33682698167332736\n",
      "特征PSI: \n",
      " credit_info    0.098585\n",
      "act_info       0.122049\n",
      "person_info    0.127833\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MIRABE~1\\AppData\\Local\\Temp/ipykernel_15092/580626469.py:3: FutureWarning: In a future version of pandas all arguments of Series.sort_values will be keyword-only\n",
      "  print('特征PSI:','\\n',toad.metrics.PSI(x,offx).sort_values(0))\n"
     ]
    }
   ],
   "source": [
    "#计算模型PSI和单变量PSI\n",
    "print('模型PSI:',toad.metrics.PSI(prob_dev,prob_off))  \n",
    "print('特征PSI:','\\n',toad.metrics.PSI(x,offx).sort_values(0))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>bads</th>\n",
       "      <th>goods</th>\n",
       "      <th>total</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>bad_prop</th>\n",
       "      <th>good_prop</th>\n",
       "      <th>...</th>\n",
       "      <th>cum_bad_rate_rev</th>\n",
       "      <th>cum_bads_prop</th>\n",
       "      <th>cum_bads_prop_rev</th>\n",
       "      <th>cum_goods_prop</th>\n",
       "      <th>cum_goods_prop_rev</th>\n",
       "      <th>cum_total_prop</th>\n",
       "      <th>cum_total_prop_rev</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.003847</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>0.002234</td>\n",
       "      <td>0.997766</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.085639</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085639</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076493</td>\n",
       "      <td>0.108796</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.006219</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.998760</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.102959</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022212</td>\n",
       "      <td>0.015244</td>\n",
       "      <td>0.990854</td>\n",
       "      <td>0.188598</td>\n",
       "      <td>0.914361</td>\n",
       "      <td>0.185039</td>\n",
       "      <td>0.915931</td>\n",
       "      <td>0.173355</td>\n",
       "      <td>0.060390</td>\n",
       "      <td>1.081799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006260</td>\n",
       "      <td>0.008478</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1755.0</td>\n",
       "      <td>1768.0</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>0.992647</td>\n",
       "      <td>0.007407</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.112162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024810</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>0.984756</td>\n",
       "      <td>0.300761</td>\n",
       "      <td>0.811402</td>\n",
       "      <td>0.295712</td>\n",
       "      <td>0.814961</td>\n",
       "      <td>0.245882</td>\n",
       "      <td>0.358120</td>\n",
       "      <td>1.208348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.008760</td>\n",
       "      <td>0.010726</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.007407</td>\n",
       "      <td>0.992593</td>\n",
       "      <td>0.007463</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>0.102767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027553</td>\n",
       "      <td>0.091463</td>\n",
       "      <td>0.945122</td>\n",
       "      <td>0.403528</td>\n",
       "      <td>0.699239</td>\n",
       "      <td>0.397121</td>\n",
       "      <td>0.704288</td>\n",
       "      <td>0.312064</td>\n",
       "      <td>0.360772</td>\n",
       "      <td>1.341954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011772</td>\n",
       "      <td>0.017231</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1478.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>0.015979</td>\n",
       "      <td>0.984021</td>\n",
       "      <td>0.016238</td>\n",
       "      <td>0.073171</td>\n",
       "      <td>0.094459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030942</td>\n",
       "      <td>0.164634</td>\n",
       "      <td>0.908537</td>\n",
       "      <td>0.497987</td>\n",
       "      <td>0.596472</td>\n",
       "      <td>0.491142</td>\n",
       "      <td>0.602879</td>\n",
       "      <td>0.333353</td>\n",
       "      <td>0.778231</td>\n",
       "      <td>1.506995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.017866</td>\n",
       "      <td>0.018114</td>\n",
       "      <td>6.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>0.014458</td>\n",
       "      <td>0.985542</td>\n",
       "      <td>0.014670</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033706</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.835366</td>\n",
       "      <td>0.524126</td>\n",
       "      <td>0.502013</td>\n",
       "      <td>0.517121</td>\n",
       "      <td>0.508858</td>\n",
       "      <td>0.341199</td>\n",
       "      <td>0.704158</td>\n",
       "      <td>1.641650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.018115</td>\n",
       "      <td>0.028989</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2842.0</td>\n",
       "      <td>2897.0</td>\n",
       "      <td>0.018985</td>\n",
       "      <td>0.981015</td>\n",
       "      <td>0.019353</td>\n",
       "      <td>0.167683</td>\n",
       "      <td>0.181632</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034742</td>\n",
       "      <td>0.350610</td>\n",
       "      <td>0.817073</td>\n",
       "      <td>0.705758</td>\n",
       "      <td>0.475874</td>\n",
       "      <td>0.698466</td>\n",
       "      <td>0.482879</td>\n",
       "      <td>0.355149</td>\n",
       "      <td>0.924658</td>\n",
       "      <td>1.692085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.029481</td>\n",
       "      <td>0.037088</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1549.0</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>0.029449</td>\n",
       "      <td>0.970551</td>\n",
       "      <td>0.030342</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.098997</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044218</td>\n",
       "      <td>0.493902</td>\n",
       "      <td>0.649390</td>\n",
       "      <td>0.804755</td>\n",
       "      <td>0.294242</td>\n",
       "      <td>0.798372</td>\n",
       "      <td>0.301534</td>\n",
       "      <td>0.310852</td>\n",
       "      <td>1.434274</td>\n",
       "      <td>2.153624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.037455</td>\n",
       "      <td>0.057047</td>\n",
       "      <td>34.0</td>\n",
       "      <td>842.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>0.038813</td>\n",
       "      <td>0.961187</td>\n",
       "      <td>0.040380</td>\n",
       "      <td>0.103659</td>\n",
       "      <td>0.053812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051537</td>\n",
       "      <td>0.597561</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.858567</td>\n",
       "      <td>0.195245</td>\n",
       "      <td>0.853208</td>\n",
       "      <td>0.201628</td>\n",
       "      <td>0.261006</td>\n",
       "      <td>1.890348</td>\n",
       "      <td>2.510062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.061511</td>\n",
       "      <td>0.093339</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2213.0</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>0.943710</td>\n",
       "      <td>0.059648</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>0.141433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.141433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146792</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.741562</td>\n",
       "      <td>2.741562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        min       max   bads   goods   total  bad_rate  good_rate      odds  \\\n",
       "0  0.001873  0.003847    3.0  1340.0  1343.0  0.002234   0.997766  0.002239   \n",
       "1  0.003902  0.006219    2.0  1611.0  1613.0  0.001240   0.998760  0.001241   \n",
       "2  0.006260  0.008478   13.0  1755.0  1768.0  0.007353   0.992647  0.007407   \n",
       "3  0.008760  0.010726   12.0  1608.0  1620.0  0.007407   0.992593  0.007463   \n",
       "4  0.011772  0.017231   24.0  1478.0  1502.0  0.015979   0.984021  0.016238   \n",
       "5  0.017866  0.018114    6.0   409.0   415.0  0.014458   0.985542  0.014670   \n",
       "6  0.018115  0.028989   55.0  2842.0  2897.0  0.018985   0.981015  0.019353   \n",
       "7  0.029481  0.037088   47.0  1549.0  1596.0  0.029449   0.970551  0.030342   \n",
       "8  0.037455  0.057047   34.0   842.0   876.0  0.038813   0.961187  0.040380   \n",
       "9  0.061511  0.093339  132.0  2213.0  2345.0  0.056290   0.943710  0.059648   \n",
       "\n",
       "   bad_prop  good_prop  ...  cum_bad_rate_rev  cum_bads_prop  \\\n",
       "0  0.009146   0.085639  ...          0.020532       0.009146   \n",
       "1  0.006098   0.102959  ...          0.022212       0.015244   \n",
       "2  0.039634   0.112162  ...          0.024810       0.054878   \n",
       "3  0.036585   0.102767  ...          0.027553       0.091463   \n",
       "4  0.073171   0.094459  ...          0.030942       0.164634   \n",
       "5  0.018293   0.026139  ...          0.033706       0.182927   \n",
       "6  0.167683   0.181632  ...          0.034742       0.350610   \n",
       "7  0.143293   0.098997  ...          0.044218       0.493902   \n",
       "8  0.103659   0.053812  ...          0.051537       0.597561   \n",
       "9  0.402439   0.141433  ...          0.056290       1.000000   \n",
       "\n",
       "   cum_bads_prop_rev  cum_goods_prop  cum_goods_prop_rev  cum_total_prop  \\\n",
       "0           1.000000        0.085639            1.000000        0.084069   \n",
       "1           0.990854        0.188598            0.914361        0.185039   \n",
       "2           0.984756        0.300761            0.811402        0.295712   \n",
       "3           0.945122        0.403528            0.699239        0.397121   \n",
       "4           0.908537        0.497987            0.596472        0.491142   \n",
       "5           0.835366        0.524126            0.502013        0.517121   \n",
       "6           0.817073        0.705758            0.475874        0.698466   \n",
       "7           0.649390        0.804755            0.294242        0.798372   \n",
       "8           0.506098        0.858567            0.195245        0.853208   \n",
       "9           0.402439        1.000000            0.141433        1.000000   \n",
       "\n",
       "   cum_total_prop_rev        ks      lift  cum_lift  \n",
       "0            1.000000  0.076493  0.108796  1.000000  \n",
       "1            0.915931  0.173355  0.060390  1.081799  \n",
       "2            0.814961  0.245882  0.358120  1.208348  \n",
       "3            0.704288  0.312064  0.360772  1.341954  \n",
       "4            0.602879  0.333353  0.778231  1.506995  \n",
       "5            0.508858  0.341199  0.704158  1.641650  \n",
       "6            0.482879  0.355149  0.924658  1.692085  \n",
       "7            0.301534  0.310852  1.434274  2.153624  \n",
       "8            0.201628  0.261006  1.890348  2.510062  \n",
       "9            0.146792 -0.000000  2.741562  2.741562  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成KS报告\n",
    "toad.metrics.KS_bucket(prob_off,offy,\n",
    "                       bucket=10,\n",
    "                       method='quantile') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[-inf ~ 0.02)</td>\n",
       "      <td>158.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.02 ~ 0.04)</td>\n",
       "      <td>122.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.04 ~ 0.11)</td>\n",
       "      <td>73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.11 ~ inf)</td>\n",
       "      <td>43.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[-inf ~ 0.16666666666666666)</td>\n",
       "      <td>94.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.16666666666666666 ~ 0.35897435897435903)</td>\n",
       "      <td>117.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.35897435897435903 ~ inf)</td>\n",
       "      <td>125.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-inf ~ -0.2610139784946237)</td>\n",
       "      <td>172.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.2610139784946237 ~ -0.1286774193548387)</td>\n",
       "      <td>143.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.1286774193548387 ~ -0.0537175627240143)</td>\n",
       "      <td>123.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.0537175627240143 ~ 0.013863440860215)</td>\n",
       "      <td>120.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.013863440860215 ~ 0.0626602150537634)</td>\n",
       "      <td>108.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.0626602150537634 ~ 0.078853046594982)</td>\n",
       "      <td>90.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.078853046594982 ~ inf)</td>\n",
       "      <td>75.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name                                        value   score\n",
       "0   credit_info                                [-inf ~ 0.02)  158.16\n",
       "1   credit_info                                [0.02 ~ 0.04)  122.58\n",
       "2   credit_info                                [0.04 ~ 0.11)   73.93\n",
       "3   credit_info                                 [0.11 ~ inf)   43.92\n",
       "4      act_info                 [-inf ~ 0.16666666666666666)   94.95\n",
       "5      act_info  [0.16666666666666666 ~ 0.35897435897435903)  117.49\n",
       "6      act_info                  [0.35897435897435903 ~ inf)  125.15\n",
       "7   person_info                 [-inf ~ -0.2610139784946237)  172.88\n",
       "8   person_info  [-0.2610139784946237 ~ -0.1286774193548387)  143.01\n",
       "9   person_info  [-0.1286774193548387 ~ -0.0537175627240143)  123.80\n",
       "10  person_info    [-0.0537175627240143 ~ 0.013863440860215)  120.84\n",
       "11  person_info     [0.013863440860215 ~ 0.0626602150537634)  108.88\n",
       "12  person_info     [0.0626602150537634 ~ 0.078853046594982)   90.34\n",
       "13  person_info                    [0.078853046594982 ~ inf)   75.46"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#输出评分卡\n",
    "from toad.scorecard import ScoreCard  \n",
    "card = ScoreCard(combiner=combiner, \n",
    "                    transer=t, C=0.1, \n",
    "                    class_weight='balanced', \n",
    "                    base_score=600,\n",
    "                    base_odds=35,\n",
    "                    pdo=60,\n",
    "                    rate=2)  \n",
    "card.fit(x,y)  \n",
    "final_card = card.export(to_frame=True)  \n",
    "final_card\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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